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International Journal of Computer Assisted Radiology and Surgery
Erschienen in: International Journal of Computer Assisted Radiology and Surgery 3/2021

03.02.2021 | Original Article

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

verfasst von: Luca Saba, Mohit Agarwal, Anubhav Patrick, Anudeep Puvvula, Suneet K. Gupta, Alessandro Carriero, John R. Laird, George D. Kitas, Amer M. Johri, Antonella Balestrieri, Zeno Falaschi, Alessio Paschè, Vijay Viswanathan, Ayman El-Baz, Iqbal Alam, Abhinav Jain, Subbaram Naidu, Ronald Oberleitner, Narendra N. Khanna, Arindam Bit, Mostafa Fatemi, Azra Alizad, Jasjit S. Suri

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2021

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Abstract

Background

COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans.

Methodology

Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation.

Results

Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models.

Conclusions

We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.
Vorheriger Artikel Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs
Nächster Artikel Association of AI quantified COVID-19 chest CT and patient outcome

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Metadaten
Titel
Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs
verfasst von
Luca Saba
Mohit Agarwal
Anubhav Patrick
Anudeep Puvvula
Suneet K. Gupta
Alessandro Carriero
John R. Laird
George D. Kitas
Amer M. Johri
Antonella Balestrieri
Zeno Falaschi
Alessio Paschè
Vijay Viswanathan
Ayman El-Baz
Iqbal Alam
Abhinav Jain
Subbaram Naidu
Ronald Oberleitner
Narendra N. Khanna
Arindam Bit
Mostafa Fatemi
Azra Alizad
Jasjit S. Suri
Publikationsdatum
03.02.2021
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2021
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-021-02317-0

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